Author
Listed:
- Li Zhiming
- Han Huijian
- Li Zongwei
- Zhang Rui
- Wei Li
Abstract
The generation and application of new self-media provide new ways to acquire information access for Internet users. It also provides a large amount of quality data for the accurate prediction of the Shanghai composite index. In this paper, we combined various machine learning and deep learning models with the search data of Chinese TikTok, which is related to the Shanghai composite index, to predict the Shanghai composite index. In addition, we compared and analyzed the prediction results of several machine learning and deep learning models in the short term, medium term, and long term. The results showed that the support vector regression model had the lowest mean absolute percentage error and the highest prediction accuracy in the short, medium, and long term, and the strongest robustness compared with other models. This was followed by random forest regression, which outperformed the remaining five benchmark prediction models (convolutional neural network, LSTM, GRU neural network, radial basis function neural network, extreme learning machine, and transformer model) in terms of prediction accuracy and robustness. The prediction results provide an innovative exploration of the prediction of the Shanghai composite index using self-media network search data. The prediction method provides a new research idea for macroeconomic prediction and forecasting and also enriches the theoretical research of machine learning methods in the field of macroeconomic index prediction.
Suggested Citation
Li Zhiming & Han Huijian & Li Zongwei & Zhang Rui & Wei Li, 2024.
"Predicting the Shanghai Composite Index Using Chinese TikTok Self-Media Data and Machine Learning Model in China,"
Discrete Dynamics in Nature and Society, Hindawi, vol. 2024, pages 1-12, December.
Handle:
RePEc:hin:jnddns:7201831
DOI: 10.1155/ddns/7201831
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnddns:7201831. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.